# Enhanced Local Binary Patterns for Automatic Face Recognition

## TL;DR

This paper introduces an improved local binary pattern descriptor for face recognition that is more robust to noise, illumination, and resolution variations, outperforming existing methods on benchmark datasets.

## Contribution

A novel local binary pattern descriptor considering multiple pixels and neighborhoods, enhancing robustness and accuracy in face recognition tasks.

## Key findings

- Outperforms state-of-the-art methods on UFI and FERET datasets.
- Handles single training sample and low-resolution images effectively.
- Demonstrates robustness to noise, illumination, and variances.

## Abstract

This paper presents a novel automatic face recognition approach based on local binary patterns. This descriptor considers a local neighbourhood of a pixel to compute the feature vector values. This method is not very robust to handle image noise, variances and different illumination conditions. We address these issues by proposing a novel descriptor which considers more pixels and different neighbourhoods to compute the feature vector values. The proposed method is evaluated on two benchmark corpora, namely UFI and FERET face datasets. We experimentally show that our approach outperforms state-of-the-art methods and is efficient particularly in the real conditions where the above mentioned issues are obvious. We further show that the proposed method handles well one training sample issue and is also robust to the image resolution.